2,099 research outputs found
Active Learning from Imperfect Labelers
We study active learning where the labeler can not only return incorrect
labels but also abstain from labeling. We consider different noise and
abstention conditions of the labeler. We propose an algorithm which utilizes
abstention responses, and analyze its statistical consistency and query
complexity under fairly natural assumptions on the noise and abstention rate of
the labeler. This algorithm is adaptive in a sense that it can automatically
request less queries with a more informed or less noisy labeler. We couple our
algorithm with lower bounds to show that under some technical conditions, it
achieves nearly optimal query complexity.Comment: To appear in NIPS 201
Beyond Disagreement-based Agnostic Active Learning
We study agnostic active learning, where the goal is to learn a classifier in
a pre-specified hypothesis class interactively with as few label queries as
possible, while making no assumptions on the true function generating the
labels. The main algorithms for this problem are {\em{disagreement-based active
learning}}, which has a high label requirement, and {\em{margin-based active
learning}}, which only applies to fairly restricted settings. A major challenge
is to find an algorithm which achieves better label complexity, is consistent
in an agnostic setting, and applies to general classification problems.
In this paper, we provide such an algorithm. Our solution is based on two
novel contributions -- a reduction from consistent active learning to
confidence-rated prediction with guaranteed error, and a novel confidence-rated
predictor
An Adaptive Strategy for Active Learning with Smooth Decision Boundary
We present the first adaptive strategy for active learning in the setting of
classification with smooth decision boundary. The problem of adaptivity (to
unknown distributional parameters) has remained opened since the seminal work
of Castro and Nowak (2007), which first established (active learning) rates for
this setting. While some recent advances on this problem establish adaptive
rates in the case of univariate data, adaptivity in the more practical setting
of multivariate data has so far remained elusive. Combining insights from
various recent works, we show that, for the multivariate case, a careful
reduction to univariate-adaptive strategies yield near-optimal rates without
prior knowledge of distributional parameters
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